Population Category Hierarchies

ABSTRACT

Developing a population category hierarchy can include providing a candidate category hierarchy, including a number of candidate categories, and a mapping between a number of reference pages and the number of candidate categories, including a number of mapped reference pages ( 143 ). Population usage data of the number of mapped reference pages can be obtained and used to determine a population traffic metric for each of the number of candidate categories ( 147 ). A number of population categories can be generated by using the population traffic metric of each of the number of candidate categories ( 149 ); and, a population category hierarchy can be produced including the number of population categories ( 151 ).

BACKGROUND

A user's web browsing history is a rich data source representing auser's implicit and explicit interests and intentions. Completed,recurring, and ongoing tasks of varying complexity and abstraction canbe found in a user's web browsing history and is consequently a valuableresource. Mechanisms that organize a user's web browsing history havebeen introduced. As the internet continues to become ever more essentialand the key tool for information seeking and retrieval, various webbrowsing mechanisms that organize a user's web browsing history havebeen introduced.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a method for developing a populationcategory hierarchy according to the present disclosure.

FIG. 2 illustrates a block diagram of an example of a system fordeveloping a population category hierarchy according to the presentdisclosure.

FIG. 3 illustrates a block diagram of an example of a computer-readablemedium (CRM) in communication with processing resources for developing apopulation category hierarchy according to the present disclosure.

FIG. 4 illustrates a block diagram of an example of a candidate categoryhierarchy and a mapping according to the present disclosure.

FIG. 5 illustrates a block diagram of an example of a populationcategory hierarchy and a population mapping according to the presentdisclosure.

FIG. 6 illustrates an example of a population mapping according to thepresent disclosure.

DETAILED DESCRIPTION

The present disclosure provides methods, machine readable media, andsystems for developing population category hierarchies. A mapping can beprovided between a number of reference pages and the number of candidatecategories to create a number of mapped reference pages. Populationusage data of the number of mapped reference pages can be obtained andused to determine a population traffic metric for each of the number ofcandidate categories. A number of population categories can be generatedby using the population traffic metric of each of the number ofcandidate categories; and, a population category hierarchy can beproduced, including the number of population categories.

For example, implementation of a category hierarchy started in theUnited States can include a category labeled “sports” which can includea subcategory “baseball” but not a subcategory “Australian rulesfootball.” If the category path system were ported to Australia it couldbe advantages for information to be generated that indicates “Australianrules football” should be a subcategory. In an example, the use of webbased services such as, for example, Wikipedia™ can be used to tailor acategory hierarchy to a population by the use of usage data from adefined population to properly weight categories and subcategories.Additional categories can consume resources and add complexity, butadditional tailored categories can also aid in organizing andcategorizing hierarchies.

In the present disclosure, reference is made to the accompanyingdrawings that form a part hereof, and in which is shown by way ofillustration how one or more examples of the disclosure can bepracticed. These examples are described in sufficient detail to enablethose of ordinary skill in the art to practice the examples of thisdisclosure, and it is to be understood that other examples can be usedand that process, electrical, and/or structural changes can be madewithout departing from the scope of the present disclosure.

The figures herein follow a numbering convention in which the firstdigit corresponds to the drawing figure number and the remaining digitsidentify an element or component in the drawing. Similar elements orcomponents between different figures can be identified by the use ofsimilar digits. For example, 214 can reference element “14” in FIG. 2,and a similar element can be referenced as 314 in FIG. 3. Elements shownin the various figures herein can be added, exchanged, and/or eliminatedso as to provide a number of additional examples of the presentdisclosure. In addition, the proportion and the relative scale of theelements provided in the figures are intended to illustrate the examplesof the present disclosure, and should not be taken in a limiting sense.

FIG. 1 is a flow chart illustrating an example of a method 140 fordeveloping a population category hierarchy according to one or moreexamples of the present disclosure. Examples of the present disclosureare not limited to the steps illustrated in FIG. 1. The method includesa candidate category hierarchy, including a number of candidatecategories, and a mapping provided at step 143. A candidate categoryhierarchy can include a publicly available source of ontologyinformation in which various concepts are assigned to one or morecategories. A candidate category hierarchy can be provided, for example,by a web-based service. For example, in the Wikipedia™ database, each ofthe articles is assigned a particular concept. In addition, the conceptsare assigned to particular categories and sub-categories defined by theeditors of the Wikipedia™ database. Such examples can be beneficial inproviding for a starting point for development of a population categoryhierarchy.

According to an example, some or all of the provided candidate categoryhierarchy can be manually defined. The candidate category levels thatare not manually defined can be computed from categorical informationcontained in a labeled text data source. A labeled text data sourcegenerally comprises a third-party database of articles such asWikipedia™, Freebase™, IMDB™, among others. For instance, a user candefine a category and one or more subcategories and can rely on thecandidate category levels contained in the labeled text data source forthe remaining subcategories in the hierarchy of predefined categorylevels. According to an example, a user can define the hierarchy ofpredefined candidate category levels as a tree structure and can map thecategories of the labeled text data source into the tree structure. Atree structure is a common way that can be used to represent ahierarchical nature of a structure in a graphical format. An example caninclude a recorded relevance of each concept to each category as theprobability that another article that mentions that concept would appearin that category.

A mapping is provided between the candidate categories in the candidatecategory hierarchy and a number of reference pages, including a numberof mapped reference pages at step 143. A reference page can include, forexample, in the Wikipedia™ database, the articles assigned to conceptswhich are additionally assigned to particular categories andsubcategories. A reference page could further, for example, be manuallydefined. Construction of a mapping between the number of reference pagesand the candidate categories can be accomplished by a computing device,such as the one discussed below and shown in FIG. 2. For example, thelabeled text data source corpus can be analyzed by the computing deviceto find categories for each concept by mapping the labeled text datasource categories into a category graph (e.g., a manually constructedcategory tree), find phrases related to each category by using the textof reference pages assigned to concepts of each category, find phrasesrelated to each concept by using text anchor tags which point to thatconcept, and evaluate counts of occurrences to determine the probabilitythat an occurrence of a particular phrase indicated by the text isrelevant to a particular category or a particular concept. For example,if 10% of reference pages (e.g., articles) containing the text “Tiger”are in the category “Golf”, then the probability of an input text beingin the category “Golf”, given that it contains the text “Tiger”, is 0.1.As another example, if 30% of the occurrences of the text “Tiger” linkto the article labeled with the concept “Tiger Woods”, then theprobability that the input text is related to “Tiger Woods”, given thatwe've observed it contains the text “Tiger”, is 0.3.

Population usage data of each of the number of mapped reference pagesare obtained at step 145. A population can be defined, for example, by aparticular geographic region or politically defined country borders. Apopulation can further be defined, for example, by type of device usedto access the reference page (e.g., mobile device). In an example, apopulation can be defined by a number of resources including: userdefined parameters, cell phone signals, cell phone tower signals, GlobalPosition System (GPS) device signals, Internet Service Provider (ISP)information, and web-based service information. Web-based services caninclude Wikipedia™, Freebase™, IMDB™, among others. Population usagedata can include timestamps, which can be used to estimate a time eachvisitor spent on each mapped reference page, and a total number ofvisits to each mapped reference page. Timestamps can, for example, bevisitor specific and include a time the visitor arrives at a page and atime the visitor leaves the page. In such examples, the number oftimestamps can be considered a number of sets of timestamps (e.g.,beginning and ending timestamps). The total number of visits can, forexample, be a total number of visits to a page per visitor (e.g., thenumber of times a visitor frequents a page). In another example, thetotal number of visits can be the overall total of the number of visitsto a reference page. In another example, population usage data caninclude usage data of pages related to the number of mapped referencepages. Such examples can be beneficial in providing greater informationregarding a defined population and tailoring categories to aid inorganization and categorization of hierarchies.

A population traffic metric is determined for each of the number ofcandidate categories by using the population usage data at step 147. Apopulation traffic metric can include, for example, a summation of thenumber of visits to each of the number of reference pages mapped to thecandidate category. In an example, a population traffic method can bedetermined by assigning a weight according to the timestamp populationusage data. For example, the weight given to the population trafficmetric can have a positive relationship to the estimated time eachvisitor spent on each mapped reference page.

The population traffic metric of each of the number of candidatecategories is used to generate a number of population categories at step149. In an example, candidate categories for which the populationtraffic metric falls below a chosen threshold level can be deleted tocreate the number of population categories. Population categories can,for example, be generated through rank of candidate categories bypopulation traffic metric and retaining candidate categories above athreshold. In another example, addition of candidate categories cangenerate the number of population categories. Population categories canbe generated through a merge and/or split of candidate categories. Amerge and/or split can, for example, be done according to an input froma user. For example, candidate categories with population trafficmetrics lower than a threshold to justify designation of the candidatecategory as an entire population category, but higher than a thresholdto justify deletion of the candidate category could be merged to obtaina threshold population traffic metric to justify a population category.In another example, a candidate category can have a population trafficmetric above a threshold at which the population traffic metric allowsadequate categorization. Consequently, the candidate category can, forexample, be split into multiple, population categories.

At step 151, a population category hierarchy is produced that includesthe number of population categories. In an example, the mapping can bemodified to create a population mapping, wherein the number of mappedreference pages are re-mapped to the number of population categories. Inanother example, the population mapping can be created by the use ofrelevance metrics for each of the number of population categories toidentify a relevance level for each of the number of populationcategories and the number of mapped reference pages. The mappedreference pages can, for example, be re-mapped according to therelevance level of each of the number of population categories. Forexample, a relevance metric can take into account the number of times amapped reference page is mapped in the population mapping. A user, in anexample, can provide input to approve or disapprove of a producedpopulation mapping. Such user input examples can be beneficial because auser can to take into account certain factors that the producedpopulation mapping did not in a particular instance (e.g., time of yearor current trends) and allow the user to decide to disapprove and/oralter the mapping. Another example includes repetition of any of theabove steps at a defined interval to update the number of populationcategories.

FIG. 2 illustrates a block diagram 200 of an example of acomputer-readable medium (CRM) 220 in communication with a computingdevice 212 (e.g., Java application server) having memory resources 217and processor resources of more or fewer than 214-1, 214-2, 214-3, thatcan be in communication with, and/or receive a tangible non-transitorycomputer readable medium (CRM) 220 storing a set of computer readableinstructions 215 executable by one or more of the processor resources(e.g., 214-1, 214-2, 214-3) for profiling a server, as described herein.

Memory resources 217 can include volatile and/or non-volatile memory.Volatile memory, as used herein, can include memory that depends uponpower to store information, such as various types of dynamic randomaccess memory (DRAM), among others. Non-volatile memory, as used herein,can include memory that does not depend upon power to store information.Examples of non-volatile memory can include solid state media such asflash memory, EEPROM, phase change random access memory (PCRAM),magnetic memory such as a hard disk, tape drives, floppy disk, and/ortape memory, optical discs, digital video discs (DVD), high definitiondigital versatile discs (HD DVD), compact discs (CD), and/or a solidstate drive (SSD), flash memory, etc., as well as other types ofmachine-readable media.

Processor resources can execute computer-readable instructions 215 thatare stored on an internal or external non-transitory computer-readablemedium 220. A non-transitory computer-readable medium (e.g., computerreadable medium 220), as used herein, can include volatile and/ornon-volatile memory.

The non-transitory computer-readable 220 medium can be integral, orcommunicatively coupled, to a computing device, in either in a wired orwireless manner. For example, the non-transitory computer-readablemedium can be an internal memory, a portable memory, a portable disk, ora memory located internal to another computing resource (e.g., enablingthe computer-readable instructions to be downloaded over the Internet).

The CRM 220 can be in communication with the processor resources (e.g.,214-1, 214-2, 214-3) via a communication path 276. The communicationpath 276 can be local or remote to a machine associated with theprocessor resources (214-1, 214-2, 214-3). Examples of a localcommunication path 276 can include an electronic bus internal to amachine such as a computer where the CRM 220 is one of volatile,non-volatile, fixed, and/or removable storage medium in communicationwith the processor resources (e.g., 214-1, 214-2, 214-3) via theelectronic bus. Examples of such electronic buses can include IndustryStandard Architecture (ISA), Peripheral Component Interconnect (PCI),Advanced Technology Attachment (ATA), Small Computer System Interface(SCSI), Universal Serial Bus (USB), among other types of electronicbuses and variants thereof.

In other examples, the communication path 276 can be such that the CRM220 is remote from the processor resources (e.g., 214-1, 214-2, 214-3)such as in the example of a network connection between the CRM 220 andthe processor resources (e.g., 214-1, 214-2, 214-3). That is, thecommunication path 276 can be a network connection. Examples of such anetwork connection can include a local area network (LAN), a wide areanetwork (WAN), a personal area network (PAN), and the Internet, amongothers. In such examples, the CRM 220 may be associated with a firstcomputing device and the processor resources (e.g., 214-1, 214-2, 214-3)may be associated with a second computing device 212 (e.g., a Javaapplication server).

FIG. 3 illustrates a block diagram of an example of a computing system300 for developing a population category hierarchy according to thepresent disclosure. However, examples of the present disclosure are notlimited to a particular computing system configuration. The system 300can include processor resources 314 and memory resources (e.g., volatilememory 316 and/or non-volatile memory 318) for executing instructionsstored in a tangible non-transitory medium (e.g., volatile memory 316,non-volatile memory 318, and/or computer-readable medium 320) and/or anapplication specific integrated circuit (ASIC) including logicconfigured to perform various examples of the present disclosure. Acomputer (e.g., a computing device) can include and/or receive atangible non-transitory computer-readable medium 320 storing a set ofcomputer-readable instructions (e.g., software) via an input device 322.In an example, the input device 322 can receive input from a number ofweb based services 373. As used herein, processor resources 314 caninclude one or a plurality of processors such as in a parallelprocessing system. Memory resources can include memory addressable bythe processor resources 314 for execution of computer-readableinstructions. The computer-readable medium 320 can include volatileand/or non-volatile memory such as random access memory (RAM), magneticmemory such as a hard disk, floppy disk, and/or tape memory, a solidstate drive (SSD), flash memory, phase change memory, etc. In someexamples, the non-volatile memory 318 can be a database including aplurality of physical non-volatile memory devices. In various examples,the database can be local to a particular system or remote (e.g.,including a plurality of non-volatile memory devices 318).

The processor resources 314 can control the overall operation of thesystem 300. The processor resources 314 can be connected to a memorycontroller 324, which can read and/or write data from and/or to volatilememory 316 (e.g., RAM). The memory controller 324 can include an ASICand/or a processor with its own memory resources (e.g., volatile and/ornon-volatile memory). The volatile memory 316 can include one or aplurality of memory modules (e.g., chips).

The processor resources 314 can be connected to a bus 326 to provide forcommunication between the processor resources 314, and other portions ofthe system 300. The non-volatile memory 318 can provide persistent datastorage for the system 300. The graphics controller 328 can connect to auser interface 330, which can provide an image to a user based onactivities performed by the system 300.

Each system can include a computing device including control circuitrysuch as a processor, a state machine, application specific integratedcircuit (ASIC), controller, and/or similar machine. As used herein, theindefinite articles “a” and/or “an” can indicate one or more than one ofthe named object. Thus, for example, “a processor” can include oneprocessor or more than one processor, such as a parallel processingarrangement.

The control circuitry can have a structure that provides a givenfunctionality, and/or execute computer-readable instructions that arestored on a non-transitory computer-readable medium (e.g. non-transitorycomputer-readable medium 320). The non-transitory computer-readablemedium can be integral, or communicatively coupled, to a computingdevice, in either in a wired or wireless manner. For example, thenon-transitory computer-readable medium 320 can be an internal memory, aportable memory, a portable disk, or a memory located internal toanother computing resource (e.g., enabling the computer-readableinstructions to be downloaded over the Internet). The non-transitorycomputer-readable medium 320 can have computer-readable instructions 315stored thereon that are executed by the control circuitry (e.g.,processor) to provide a particular functionality.

The non-transitory computer-readable medium, as used herein, can includevolatile and/or non-volatile memory. Volatile memory can include memorythat depends upon power to store information, such as various types ofdynamic random access memory (DRAM), among others. Non-volatile memorycan include memory that does not depend upon power to store information.Examples of non-volatile memory can include solid state media such asflash memory, EEPROM, phase change random access memory (PCRAM), amongothers. The non-transitory computer-readable medium can include opticaldiscs, digital video discs (DVD), Blu-Ray Discs, compact discs (CD),laser discs, and magnetic media such as tape drives, floppy discs, andhard drives, solid state media such as flash memory, EEPROM, phasechange random access memory (PCRAM), as well as other types ofcomputer-readable media.

FIG. 4 illustrates a block diagram of an example of a candidate categoryhierarchy 460 and a mapping 464 according to the present disclosure.Candidate category hierarchy 460 and the mapping 464 can, for example,be provided as described above (e.g., step 143 in FIG. 1). A candidatecategory hierarchy 460 can, for example, contain candidate categories462. In an example, candidate categories 462 can be linked to a numberof other candidate categories 462. For example, a candidate category 462labeled “Sports” can be linked to other candidate categories 462labeled, for example, “Baseball,” “Football,” and “Golf.” Candidatecategories 462, can be mapped to reference pages 466. Once a candidatecategory 462 has been mapped to a reference page 466, the reference page466 is referred to as a mapped reference page 468. The block diagramillustrated in FIG. 4 includes a number of reference pages 466 that arenot mapped to candidate categories 462 and a number of mapped referencepages 468 that are mapped to candidate categories 462. Candidatecategories can be mapped to reference pages 466 to create a mapping 464that contains a number of mapped reference pages 468. For example, acandidate category 462 labeled “Baseball” can be mapped to referencepages 466 such as the Minnesota Twins baseball club homepage (e.g.,www.minnesota.twins.mlb.com), the official web-site of Major Leaguebaseball (e.g., www.mlb.com), or the Wikipedia article on baseball(e.g., www.en.wikipedia.org/wiki/Baseball) to form a number of mappedreference pages 468.

Each mapped reference page 468, in an example, contains population usagedata 470, however examples are not so limited as one or more mappedreference pages 468 may not include population usage data 470 and/or areference page 466 (e.g., an unmapped reference page) may includepopulation usage data 470. Population usage data 470 can include anumber of timestamps 478 and/or a total number of visits 480 to aparticular mapped reference page 468. In an example, the populationusage data 470 can be stipulated by a number of parameters, including,but not limited to: user defined parameters 477, cell phone signals 479,cell phone tower signals 481, GPS device signals 483, ISP information485, and/or web-based service information 487. For example, thepopulation usage data 470 can be stipulated by a user defined parameter477 to contain data only regarding visitors from a specific region(e.g., Australia, Europe, North America, etc.). In an example, multipleparameters can be used to stipulate the population usage data 470.

FIG. 5 illustrates a block diagram of an example of a populationcategory hierarchy 576 and a population mapping 592 according to thepresent disclosure. Population category hierarchy 576 can, for example,be produced as described above (e.g., step 151 in FIG. 1). Prior to thepopulation mapping 592, the population usage data 570 of each mappedreference page 568 can be used to determine a population traffic metricfor each candidate category to generate a number of populationcategories 574. For example, a user can define a lower threshold limitfor a population traffic metric. In an example, the lower threshold canbe a specified total number of visits (e.g., visits 480 illustrated inFIG. 4) to all mapped reference pages 568 of a candidate category (e.g.,candidate category 462 illustrated in FIG. 4). In such an example, anycandidate category, below the specified total number of visits, can beremoved from the mapping (e.g., mapping 464 illustrated in FIG. 4) tocreate population categories 574 in a population mapping 592.

Population categories 574 of population mapping 592 can maintain thesame mapped reference pages as the candidate category or categories fromwhich they were created. For example, if the candidate category “MinorLeague Baseball Teams in Minnesota” was mapped to the mapped referencepage “St. Paul Saints” and the candidate category “Minor League BaseballTeams in Minnesota” did not receive above the minimum threshold of atotal number of visits for all mapped reference pages (e.g., “St. PaulSaints”) mapped to the candidate category, the candidate category can bemerged with a similar candidate category “Minor League Baseball Teams inthe USA” to create a population category 574 “Minor League BaseballTeams in the USA” that maintained the mapping to the mapped referencepage 568 “St. Paul Saints” as well as any existing mapped referencepages of the candidate category “Minor League Baseball Teams in theUSA.” In such an example, the mapped reference pages 568 are maintained(e.g., not re-mapped as discussed in regards to FIG. 6) whereas thecandidate categories are manipulated to form population categories 574.

The population categories 574, in an example, can be organized such thata population category 574 can be linked to other population categories574 to create a population category hierarchy 576. In another example,population usage data 570 of related pages 588 to the number of mappedreference pages 568 can be used to produce the population categoryhierarchy 576. For example, a mapped reference page 568 can be theMinnesota Twins baseball club homepage, “www.minnesota.twins.mlb.com.”Related pages 588 can include players on the Minnesota Twins baseballclub (e.g., www.joe-mauer.org, www.thisisdspan.com, orwww.twitter.com/mcuddy5) or blogs about the Minnesota Twins baseballclub (e.g., www.aarongleeman.com, www.nickstwinsblog.com, orwww.twinkietown.com). Such related pages 588 examples can be beneficialbecause the use of related pages 588 can make the population trafficmetric more tailored to a particular topic and consequently can lead tothe creation of a population category hierarchy 576 and populationmapping 592 that are more reflective of a particular population.

FIG. 6 illustrates an example of a population mapping 692 according tothe present disclosure. A population mapping can be created byre-mapping the number of mapped reference pages 668 to form the numberof population categories 674 (e.g., after step 151 in FIG. 1). Forexample, a mapped reference page 668 referencing Tiger Woods can bemapped to a candidate category “Golf” (e.g., candidate category 462illustrated in FIG. 4). The mapped reference page referencing TigerWoods 668 can, in an example, be re-mapped, based on the populationusage data 670, for example, to candidate categories “Celebrities,”“Athletes,” and/or “Sports Apparel” to create a number of populationcategories 674 in population mapping 692. Such re-mapping examples canbe beneficial to update a population mapping 692 or make a morecomprehensive population mapping 692.

Although specific examples have been illustrated and described herein,those of ordinary skill in the art will appreciate that an arrangementcalculated to achieve the same results can be substituted for thespecific examples shown. This disclosure is intended to coveradaptations or variations of one or more examples of the presentdisclosure. It is to be understood that the above description has beenmade in an illustrative fashion, and not a restrictive one. Combinationof the above examples, and other examples not specifically describedherein will be apparent to those of skill in the art upon reviewing theabove description. The scope of the one or more examples of the presentdisclosure includes other applications in which the above structures andmethods are used. Therefore, the scope of one or more examples of thepresent disclosure should be determined with reference to the appendedclaims, along with the full range of equivalents to which such claimsare entitled.

Throughout the specification and claims, the meanings identified belowdo not necessarily limit the terms, but merely provide illustrativeexamples for the terms. The meaning of “a,” “an,” and “the” includesplural reference, and the meaning of “in” includes “in” and “on.” Theterm “a number of” is meant to be understood as including at least onebut not limited to one. The phrase “in an example,” as used herein doesnot necessarily refer to the same example, although it can.

What is claimed:
 1. A method for developing a population category hierarchy, comprising: providing a candidate category hierarchy, including a number of candidate categories, and a mapping between a number of reference pages and the number of candidate categories, including a number of mapped reference pages; obtaining population usage data of each of the number of mapped reference pages; using the population usage data to determine a population traffic metric for each of the number of candidate categories; generating a number of population categories by using the population traffic metric of each of the number of candidate categories; and producing a population category hierarchy including the number of population categories.
 2. The method of claim 1, wherein the population usage data includes a number of timestamps and a total number of visits to each of the number of mapped reference pages wherein the number of timestamps are used to estimate a time for each of a number of visitors spent on each mapped reference page.
 3. The method of claim 2, wherein determining the population traffic metric includes weighting according to the number of timestamps, wherein the weight given and the estimated time each visitor spent on each mapped reference page is a positive relationship.
 4. The method of claim 2, wherein determining the population traffic metric includes summing the total number of visits to each of the number of mapped reference pages to a particular one of the number of candidate categories.
 5. The method of claim 1, wherein the method further includes modifying the mapping to create a population mapping, wherein the number of mapped reference pages are re-mapped to form the number of population categories to create the population mapping.
 6. The method of claim 5, wherein modifying includes: using relevance metrics for each of the number of population categories to identify a relevance level for each of the number of population categories and the number of mapped reference pages; and re-mapping the number of mapped reference pages according to the relevance level of each of the number of population categories.
 7. The method of claim 1, wherein generating the number of population categories includes reducing or merging candidate categories.
 8. The method of claim 1, wherein generating the number of population categories includes adding or splitting candidate categories.
 9. The method of claim 1, wherein generating the number of population categories includes: receiving an input from a user; merging candidate categories; and splitting candidate categories.
 10. The method of claim 1, wherein the method includes obtaining the candidate category hierarchy and the population usage data from a number of web-based services.
 11. The method of claim 1, wherein the method includes defining the population according to at least one of a number of user defined parameters, cell phone signals, cell phone tower signals, Global Positioning Satellite (GPS) device signals, Internet Service Provider (ISP) information, and web-based service information.
 12. A non-transitory computer-readable medium storing a set of instructions executable by a processor to: provide a candidate category hierarchy, including a number of candidate categories, and a mapping between a number of reference pages and the number of candidate categories, including a number of mapped reference pages; obtain population usage data of each of the number of mapped reference pages; use the population usage data to determine a population traffic metric for each of the number of candidate categories; generate a number of population categories by using the population traffic metric of each of the number of candidate categories; produce a population category hierarchy including the number of population categories; and modify the mapping to create a population mapping, wherein the number of mapped reference pages are re-mapped to the number of population categories to create the population mapping.
 13. The non-transitory computer-readable medium of claim 12, wherein the medium further includes instructions to use population usage data of pages related to the number of mapped reference pages to produce the population category hierarchy.
 14. The non-transitory computer-readable medium of claim 12, wherein the instructions to modify the mapping further include instructions to: use relevance metrics for each of the number of population categories to identify a relevance level for each of the number of population categories and the number of mapped reference pages; and re-map the number of mapped reference pages according to the relevance level of each of the number of population categories.
 15. A system for developing a population category hierarchy, comprising: a computing device including: a memory; and a number of processors coupled to the memory, to: provide a candidate category hierarchy, including a number of candidate categories, and a mapping between a number of reference pages and the number of candidate categories, including a number of mapped reference pages; obtain population usage data of each of the number of mapped reference pages; use the population usage data to determine a population traffic metric for each of the number of candidate categories; generate a number of population categories by using the population traffic metric of each of the number of candidate categories; produce a population category hierarchy including the number of population categories; and modify the mapping to create a population mapping, wherein the number of mapped reference pages are re-mapped to the number of population categories to create the population mapping. 